Diluting the Scalability Boundaries: Exploring the Use of Disaggregated Architectures for High-Level Network Data Analysis

Traditional data centers are designed with a rigid architecture of fit-for-purpose servers that provision resources beyond the average workload in order to deal with occasional peaks of data. Heterogeneous data centers are pushing towards more cost-efficient architectures with better resource provisioning. In this paper we study the feasibility of using disaggregated architectures for intensive data applications, in contrast to the monolithic approach of server-oriented architectures. Particularly, we have tested a proactive network analysis system in which the workload demands are highly variable. In the context of the dReDBox disaggregated architecture, the results show that the overhead caused by using remote memory resources is significant, between 66\% and 80\%, but we have also observed that the memory usage is one order of magnitude higher for the stress case with respect to average workloads. Therefore, dimensioning memory for the worst case in conventional systems will result in a notable waste of resources. Finally, we found that, for the selected use case, parallelism is limited by memory. Therefore, using a disaggregated architecture will allow for increased parallelism, which, at the same time, will mitigate the overhead caused by remote memory.Comment: 8 pages, 6 figures, 2 tables, 32 references. Pre-print. The paper will be presented during the IEEE International Conference on High Performance Computing and Communications in Bangkok, Thailand. 18 - 20 December, 2017. To be published in the conference proceeding

Insulin gene polymorphisms in type I diabetes, Addison's disease and the polyglandular autoimmune syndrome type II

Background: Polymorphisms within the insulin gene can influence insulin expression in the pancreas and especially in the thymus, where self-antigens are processed, shaping the T cell repertoire into selftolerance, a process that protects from ß-cell autoimmunity. Methods: We investigated the role of the -2221Msp(C/T) and -23HphI(A/T) polymorphisms within the insulin gene in patients with a monoglandular autoimmune endocrine disease [patients with isolated type 1 diabetes (T1D, n = 317), Addison´s disease (AD, n = 107) or Hashimoto´s thyroiditis (HT, n = 61)], those with a polyglandular autoimmune syndrome type II (combination of T1D and/or AD with HT or GD, n = 62) as well as in healthy controls (HC, n = 275). Results: T1D patients carried significantly more often the homozygous genotype "CC" -2221Msp(C/T) and "AA" -23HphI(A/T) polymorphisms than the HC (78.5% vs. 66.2%, p = 0.0027 and 75.4% vs. 52.4%, p = 3.7 × 10-8, respectively). The distribution of insulin gene polymorphisms did not show significant differences between patients with AD, HT, or APS-II and HC. Conclusion: We demonstrate that the allele "C" of the -2221Msp(C/T) and "A" -23HphI(A/T) insulin gene polymorphisms confer susceptibility to T1D but not to isolated AD, HT or as a part of the APS-II

Automatic food intake detection based on swallowing sounds

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of \u3e80% and \u3e75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30 s. Results obtained on 44.1 h of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions

Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing

The number of distinct foods consumed in a meal is of significant clinical concern in the study of obesity and other eating disorders. This paper proposes the use of information contained in chewing and swallowing sequences for meal segmentation by food types. Data collected from experiments of 17 volunteers were analyzed using two different clustering techniques. First, an unsupervised clustering technique, Affinity Propagation (AP), was used to automatically identify the number of segments within a meal. Second, performance of the unsupervised AP method was compared to a supervised learning approach based on Agglomerative Hierarchical Clustering (AHC). While the AP method was able to obtain 90% accuracy in predicting the number of food items, the AHC achieved an accuracy \u3e95%. Experimental results suggest that the proposed models of automatic meal segmentation may be utilized as part of an integral application for objective Monitoring of Ingestive Behavior in free living conditions

A Microservice Infrastructure for Distributed Communities of Practice

Non-formal learning in Communities of Practice (CoPs) makes up a significant portion of today’s knowledge gain. However, only little technological support is tailored specifically towards CoPs and their particular strengths and challenges. Even worse, CoPs often do not possess the resources to host or even develop a software ecosystem to support their activities. In this paper, we describe a distributed, microservice-based Web infrastructure for non-formal learning in CoPs. It mitigates the need for central infrastructures, coordination or facilitation and takes into account the constant change of these communities. As a real use case, we implement an inquiry-based learning application on-top of our infrastructure. Our evaluation results indicate the usefulness of this learning application, which shows promise for future work in the domain of community-hosted, microservice-based Web infrastructures for learning outside of formal settings

An Exploratory Study of Lecturers' Views of Out-of-class Academic Collaboration Among Students

This article reports an exploratory study of lecturers' perceptions of out-of-class academic collaboration (OCAC) among students at a large Singapore university. Two types of OCAC were investigated: collaboration initiated by students, e.g., groups decide on their own to meet to prepare for exams, and collaboration required by teachers, e.g., teachers assign students to do projects in groups. Data were collected via one-on-one interviews with 18 faculty members from four faculties at the university. Findings suggest that OCAC, especially of a teacher-required kind, is fairly common at the university. Faculty members' views on factors affecting the success of OCAC are discussed for the light they might shed on practices to enhance the effectiveness of OCAC

Post-traumatic stress disorder in parturients delivering by caesarean section and the implication of anaesthesia: a prospective cohort study.

Post-traumatic stress disorder (PTSD) occurs in 1-7% of women following childbirth. While having a caesarean section (C-section) is known to be a significant risk factor for postpartum PTSD, it is currently unknown whether coexisting anaesthesia-related factors are also associated to the disorder. The aim of this study was to assess anaesthesia-linked factors in the development of acute postpartum PTSD. We performed a prospective cohort study on women having a C-section in a tertiary hospital in Switzerland. Patients were followed up six weeks postpartum. Patient and procedure characteristics, past morbidity or traumatic events, psychosocial status and stressful perinatal events were measured. Outcome was divided into two categories: full PTSD disease and PTSD profile. This was based on the number of DSM-IV criteria of the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) present. The PTSD Checklist Scale and the Clinician Administered PTSD Scale were used for measurement. Of the 280 patients included, 217 (77.5%) answered the questionnaires and 175 (62.5%) answered to an additional phone interview. Twenty (9.2%) had a PTSD profile and six (2.7%) a PTSD. When a full predictive model of risk factors for PTSD profile was built using logistic regression, maternal prepartum and intrapartum complications, anaesthetic complications and dissociative experiences during C-section were found to be the significant predictors for PTSD profile. This is the first study to show in parturients having a C-section that an anaesthesia complication is an independent risk factor for postpartum PTSD and PTSD profile development, in addition to known perinatal and maternal risk factors

CENDARI Virtual Research Environment & Named Entity Recognition techniques

International audienceCENDARI (Collaborative European Digital Archive Infrastructure) is a research infrastructure project aimed at integrating digital archives and resources for research on medieval and modern European history.The project brings together information and computer scientists with historians and existing historical research infrastructures (archives, libraries, other digital projects) to improve conditions for digital historical scholarship. CENDARIhas engaged in extensive networking with the archives and libraries of Europe, especially those in Eastern Europe.CENDARI is a 4-year, European-Commission-funded project led by Trinity College Dublin, in partnership with 14 institutions across 8 countries